
import operator as op


def 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[str]					=		[]
  __SCREAMING_SNAKE_CASE      :	Dict					=		lambda snake_case       ,			snake_case							: int(x / y							)  # noqa: E731 integer division operation
  __SCREAMING_SNAKE_CASE      :	str					=		{
      '''^''': op.pow,
      '''*''': op.mul,
      '''/''': div,
      '''+''': op.add,
      '''-''': op.sub,
  }  # operators & their respective operation

  # print table header
  print('''Symbol'''.center(8							)       ,			'''Action'''.center(12							)       ,			'''Stack'''       ,			sep=''' | '''							)
  print('''-''' * (30 + len(snake_case							))							)

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(snake_case							)  # append x to stack
      # output in tabular format
      print(x.rjust(8							)       ,			('''push(''' + x + ''')''').ljust(12							)       ,			''','''.join(snake_case							)       ,			sep=''' | '''							)
    else:
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		stack.pop()  # pop stack
      # output in tabular format
      print(''''''.rjust(8							)       ,			('''pop(''' + b + ''')''').ljust(12							)       ,			''','''.join(snake_case							)       ,			sep=''' | '''							)

      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		stack.pop()  # pop stack
      # output in tabular format
      print(''''''.rjust(8							)       ,			('''pop(''' + a + ''')''').ljust(12							)       ,			''','''.join(snake_case							)       ,			sep=''' | '''							)

      stack.append(
          str(opr[x](int(snake_case							)       ,			int(snake_case							)							)							)							)  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8							)       ,			('''push(''' + a + x + b + ''')''').ljust(12							)       ,			''','''.join(snake_case							)       ,			sep=''' | '''       ,			)

  return int(stack[0]							)


if __name__ == "__main__":
   lowercase_ 				=			input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
   print("""\n\tResult = """, solve(Postfix))

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


A						=			logging.get_logger(__name__)





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['pixel_values']
		def __init__(       self     :     List[str]     ,  snake_case__     :     bool = True     ,  snake_case__     :     Dict[str, int] = None     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[int, float] = 1 / 2_5_5     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     bool = True     ,  **snake_case__     :     List[Any]     ,  )      ->	None:
				super().__init__(**snake_case__		)
				_lowerCamelCase				   =  size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  do_resize
				_lowerCamelCase				   =  size
				_lowerCamelCase				   =  resample
				_lowerCamelCase				   =  do_rescale
				_lowerCamelCase				   =  rescale_factor
				_lowerCamelCase				   =  do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				_lowerCamelCase				   =  image_std if image_std is not None else OPENAI_CLIP_STD
				_lowerCamelCase				   =  do_convert_rgb
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Dict[str, int]     ,  snake_case__     :     PILImageResampling = PILImageResampling.BICUBIC     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Dict     ,  )      ->	np.ndarray:
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)
				if "height" not in size or "width" not in size:
						raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				_lowerCamelCase				   =  (size['height'], size['width'])
				return resize(snake_case__     ,  size=snake_case__     ,  resample=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[int, float]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	int:
				return rescale(snake_case__     ,  scale=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     np.ndarray     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Union[float, List[float]]     ,  snake_case__     :     Optional[Union[str, ChannelDimension]] = None     ,  **snake_case__     :     Optional[int]     ,  )      ->	np.ndarray:
				return normalize(snake_case__     ,  mean=snake_case__     ,  std=snake_case__     ,  data_format=snake_case__     ,  **snake_case__		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     ImageInput     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Dict[str, int]] = None     ,  snake_case__     :     PILImageResampling = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[float] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[float, List[float]]] = None     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  snake_case__     :     bool = None     ,  snake_case__     :     ChannelDimension = ChannelDimension.FIRST     ,  **snake_case__     :     str     ,  )      ->	PIL.Image.Image:
				_lowerCamelCase				   =  do_resize if do_resize is not None else self.do_resize
				_lowerCamelCase				   =  resample if resample is not None else self.resample
				_lowerCamelCase				   =  do_rescale if do_rescale is not None else self.do_rescale
				_lowerCamelCase				   =  rescale_factor if rescale_factor is not None else self.rescale_factor
				_lowerCamelCase				   =  do_normalize if do_normalize is not None else self.do_normalize
				_lowerCamelCase				   =  image_mean if image_mean is not None else self.image_mean
				_lowerCamelCase				   =  image_std if image_std is not None else self.image_std
				_lowerCamelCase				   =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				_lowerCamelCase				   =  size if size is not None else self.size
				_lowerCamelCase				   =  get_size_dict(snake_case__     ,  default_to_square=snake_case__		)

				_lowerCamelCase				   =  make_list_of_images(snake_case__		)

				if not valid_images(snake_case__		):
						raise ValueError(
						    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
						    'torch.Tensor, tf.Tensor or jax.ndarray.'		)

				if do_resize and size is None or resample is None:
						raise ValueError('Size and resample must be specified if do_resize is True.'		)

				if do_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.'		)

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('Image mean and std must be specified if do_normalize is True.'		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						_lowerCamelCase				   =  [convert_to_rgb(snake_case__		) for image in images]

				# All transformations expect numpy arrays.
				_lowerCamelCase				   =  [to_numpy_array(snake_case__		) for image in images]

				if do_resize:
						_lowerCamelCase				   =  [self.resize(image=snake_case__     ,  size=snake_case__     ,  resample=snake_case__		) for image in images]

				if do_rescale:
						_lowerCamelCase				   =  [self.rescale(image=snake_case__     ,  scale=snake_case__		) for image in images]

				if do_normalize:
						_lowerCamelCase				   =  [self.normalize(image=snake_case__     ,  mean=snake_case__     ,  std=snake_case__		) for image in images]

				_lowerCamelCase				   =  [to_channel_dimension_format(snake_case__     ,  snake_case__		) for image in images]

				_lowerCamelCase				   =  BatchFeature(data={'pixel_values': images}     ,  tensor_type=snake_case__		)

				return encoded_outputs